Dealing with class imbalance

A classifier is only as good as the data that is used for training. A common problem faced in the real world is issues with data quality. For a classifier to perform well, it needs to see an equal number of points for each class. But when data is collected in the real world, it's not always possible to ensure that each class has the exact same number of data points. If one class has 10 times the number of data points than another class, then the classifier tends to get biased towards the more numerous class. Hence, we need to make sure that we account for this imbalance algorithmically. Let's see how to do that.

Create a new Python file and import the following packages:

import sys

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

from utilities...